Gyu Hwan Park
Variational Inference for Bayesian Nonnegative Matrix Factorisation
As an alternative approach to markov chain monte carlo, variational inference approximates the posterior distribution using an optimisation approach. Specifically, given a family of probability distributions, variational inference aims to find a member which is most similar to the posterior distribution. In this research project, I explore an extension of variational inference, the structured stochastic variational inference. I aim to develop a novel structured stochastic variational inference algorithm for a sparse non-negative matrix factorisation model and apply it to a single-cell RNA-seq dataset to identify meaningful biological processes hidden in the data.
Gyu Hwan Park
The University of Melbourne
Gyu Hwan Park is a third-year Bachelor of Science student at the University of Melbourne, majoring in Mathematics & Statistics. He is intrigued by the intersection of modern applied Statistics and Computing in Statistical Machine Learning, Deep Learning and Bayesian Inference, particularly for their ever-increasing impacts in today’s world. With such interests, Gyu Hwan desires to develop further expertise and contribute to meaningful research in the future.
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